Structured Abstract - Example 1

Population mortality forecasts are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of public pensions, private pensions, and health care financing systems. Although we know a great deal about patterns in and causes of mortality, most forecasts are still based on simple linear extrapolations that ignore covariates and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known health and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporate, in a formal statistical model, the effects of the recent rapid increase in obesity, the steady decline in tobacco consumption, and the well known patterns of smooth mortality age profiles and time trends. Formally including new information in forecasts can matter a great deal. For example, we estimate an increase in male life expectancy at birth from 76.2 years in 2010 to 79.9 years in 2030, which is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next twenty years from 80.5 years to 81.9 years, which is virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections. We show that these patterns are also likely to greatly affect the aging American population structure. We offer an easy-to-use approach so that researchers can include other sources of information and potentially improve on our forecasts too.

Structured Abstract

BACKGROUND
Population mortality forecasts are widely used for allocating public health expenditures and other public policy purposes. Most forecasts are still based on simple linear extrapolations that ignore covariates and other prior information.

OBJECTIVE
We wish to improve mortality forecast methods, by designing models that incorporate prior knowledge about patterns and causes of mortality.

METHODS
We develop a Bayesian forecasting model capable of including known health and demographic information. This leads to age- and sex-specific forecasts of American mortality that simultaneously incorporate the effects of rapidly increasing obesity, declining tobacco consumption, and well-known patterns of smooth mortality age profiles and time trends.

RESULTS
We estimate an increase in male life expectancy at birth over 2010-2030 that is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at that are virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections.

CONCLUSIONS
Including prior information in forecasts can matter a great deal. We show that the mortality patterns captured by our model are likely to greatly affect the aging American population structure.

CONTRIBUTION
We demonstrate a new, easy-to-use approach to mortality forecasting that allows researchers to improve forecasts by incorporating external sources of information.